Supercharge your Prometheus with Cortex 101

Table of Contents

1. Introduction
2. About Cortex
3. Why Use Cortex
4. Practical Use Cases
5. Companies Using Cortex
6. How Cortex Works
7. Ingesting Data into Cortex
8. Querying Data from Cortex
9. Rule Evaluation and Alerting in Cortex
10. Long-Term Storage in Cortex
11. Experimental Features in Cortex
12. Comparing Cortex with Thanos
13. Choosing Between Cortex and Thanos
14. Migrating from Prometheus to Cortex
15. Selective Retention in Cortex
16. Conclusion
17. Frequently Asked Questions (FAQ)

Introduction

Welcome to the world of Cortex! In this guide, we will explore everything You need to know about Cortex, a powerful data storage and analysis system. Whether you're new to Cortex or already familiar with its capabilities, this guide will provide you with a comprehensive understanding of the platform. From its features and use cases to its architecture and integration options, we've got you covered. So, let's dive in and discover how Cortex can revolutionize your data management and analysis processes.

About Cortex

Cortex is a state-of-the-art data storage and analysis system designed to solve the challenges of storing and querying metrics data at Scale. Maintained by a team of passionate developers, Cortex offers seamless integration with Prometheus, making it an ideal choice for users who want to scale their Prometheus deployments across multiple environments or clusters. With its global store of metrics, Durable long-term storage, and multi-tenancy support, Cortex provides a centralized solution for aggregating and analyzing data from diverse sources.

Why Use Cortex

There are several compelling reasons why you should consider using Cortex for your metrics storage and analysis needs. Here are a few key advantages of using Cortex:

1. Scalability: Cortex offers horizontal scalability, allowing you to handle increasing volumes of metrics data without sacrificing performance. You can easily scale the various components of Cortex to meet the demands of your growing infrastructure.

2. Durable Long-Term Storage: With Cortex, you no longer have to worry about limited local disk storage. It provides a global store of as many metrics as you need, ensuring that you have access to historical data for future analysis and troubleshooting.

3. Multiple Tenancy Support: Cortex's unique multi-tenancy feature allows you to isolate data between different customers or internal products. This ensures data privacy and enables efficient data analysis for different user groups.

4. Flexible Use Cases: Cortex caters to a wide range of use cases. Whether you need a centralized system for aggregating data from isolated clusters or want to provide Prometheus as a service to your teams, Cortex can adapt to your requirements.

Practical Use Cases

Now that you understand the benefits of using Cortex, let's explore some practical use cases where Cortex can be a game-changer. These use cases demonstrate the versatility and power of Cortex in real-world scenarios:

1. Long-Term Storage: If you need a reliable and scalable solution for storing large volumes of metrics data over an extended period, Cortex is an excellent choice. It offers long-term storage capabilities and can handle the data retention requirements of diverse industries.

2. Centralized Data Aggregation: Cortex can serve as a central system for aggregating data from multiple isolated clusters or environments. This functionality is particularly useful when you want to compare metrics across different systems or evaluate the performance of individual clusters.

3. Prometheus as a Service: Many organizations use Cortex to provide Prometheus as a service to their development teams. By leveraging Cortex's multi-tenancy support, organizations can efficiently and securely manage Prometheus deployments for different teams or customers.

4. Custom Metrics Analysis: Cortex's query and alerting capabilities make it an ideal platform for custom metrics analysis. Whether you need to monitor specific metrics or build complex dashboards, Cortex offers the flexibility and power to meet your analytical needs.

These use cases are just a glimpse of what Cortex can do. You can get creative and explore many other innovative applications of Cortex in your data management and analysis workflows.

Companies Using Cortex

Cortex has gained popularity among various companies and organizations that require a robust and scalable metrics storage solution. Here are some examples of companies leveraging the capabilities of Cortex:

1. WeWork: WeWork, a global real estate company, is a major user of Cortex. Cortex originated from WeWork's internal infrastructure needs, and they Continue to use Cortex to store and analyze their vast amounts of metrics data.

2. Kevanna Labs: Kevanna Labs, a technology company specializing in artificial intelligence and machine learning, relies on Cortex to power their internal metrics analysis processes. Cortex's scalability and multi-tenancy features make it an ideal choice for Kevanna Labs.

3. Aspen Mesh: Aspen Mesh, a provider of service mesh solutions, uses Cortex to aggregate data from their distributed systems. Cortex's long-term storage capabilities allow Aspen Mesh to access historical data for in-depth analysis and troubleshooting.

4. Electronic Arts (EA): The gaming giant Electronic Arts utilizes Cortex to manage and analyze their internal metrics. Cortex provides the infrastructure they need to monitor and optimize their gaming platforms and services.

These are just a few examples of companies that have recognized the value of Cortex in their metrics analysis workflows. Cortex's proven track Record and extensive user base make it a reliable and trusted solution in the industry.

How Cortex Works

To fully understand Cortex's capabilities, it's essential to have a clear understanding of how the system works. Cortex builds on the codebase of Prometheus and extends its functionalities to provide scalable metrics storage and analysis. Let's dive into the key components and processes that make up the Cortex ecosystem.

Ingesting Data into Cortex

The process of ingesting data into Cortex involves several components working together seamlessly. Here's a high-level overview of the steps involved:

1. Distributor: The distributor component acts as the entry point for metrics data in Cortex. It receives data from Prometheus instances and performs deduplication, ensuring that only one set of data is persisted for each series. The distributor sends the data to the ingestors Based on a consistent hashing mechanism, ensuring that data for a specific series is always directed to the same ingestor.

2. Ingestors: The ingestors compress the samples received from the distributor into chunks using the same compression algorithm as Prometheus. These chunks are stored in memory until they are flushed to the long-term storage, such as DynamoDB or BigTable, after a specific time duration or when no data is received for a series.

3. Long-Term Storage: Cortex leverages various storage options, including DynamoDB, BigTable, and blob storage (e.g., S3 or Google Cloud Storage), for long-term storage of data. The storage choice depends on factors like cost, scalability requirements, and the Type of data being stored. Cortex separates the index data and chunk data, making it flexible to store each type in different storage systems for optimized performance.

Querying Data from Cortex

Cortex provides robust querying capabilities, allowing users to retrieve and analyze metrics data efficiently. Here's an overview of the querying process:

1. Query Front-End: The query front-end component offers optional functionality that enhances query performance and resource management. It splits long-range queries into smaller parts and executes them in Parallel, ensuring faster execution and resource optimization. The query front-end also handles query caching and fair scheduling of queries across different tenants to prevent any single tenant from monopolizing system resources.

2. Query Engine: The query engine executes queries by leveraging the embedded PromQL engine from Prometheus. It retrieves recent data from the ingestors and fetches historical data from the long-term storage, if required. The query engine merges the data from different sources and performs the necessary calculations and transformations before returning the results to the user.

3. Caching: Cortex incorporates multiple layers of caching to optimize query performance. Caching improves query response times by storing frequently accessed data in memory. This reduces the need to fetch data from the ingestors or long-term storage for subsequent identical queries.

Rule Evaluation and Alerting in Cortex

Cortex offers comprehensive rule evaluation and alerting features, allowing users to define and monitor custom rules for their metrics data. Here's an overview of how rule evaluation and alerting work in Cortex:

1. Ruler Component: The ruler component evaluates recording and alerting rules defined by users. It retrieves recent data from the ingestors and long-term storage and applies the specified rules to generate the desired output. The results of the rule evaluation are then written back to the ingestors for efficient querying.

2. Alert Manager: Cortex incorporates the Prometheus Alert Manager, which handles the management and dispatching of alerts. The Alert Manager, with its multi-tenancy wrappers, allows users to send alerts to various channels, such as email, Slack, or pager duty, based on their predefined alerting rules.

The combination of powerful rule evaluation and alerting capabilities ensures that users can efficiently monitor their metrics data and receive Timely notifications for any anomalies or issues.

Long-Term Storage in Cortex

Cortex provides flexible options for long-term storage of metrics data, allowing users to choose the solution that best fits their needs. Here are the storage options supported by Cortex:

1. AWS Stack: For users in AWS environments, Cortex offers integration with DynamoDB and S3 for storing index data and chunk data, respectively. DynamoDB provides a scalable and managed NoSQL store for efficient metadata storage, while S3 allows cost-effective and durable storage of large chunks of data.

2. Google Cloud Stack: Users in Google Cloud environments can leverage BigTable for storing index data and Google Cloud Storage for chunk data. BigTable offers high-performance NoSQL storage, while Google Cloud Storage provides reliable and scalable blob storage for data chunks.

3. Cassandra: Cortex also supports Cassandra as a storage option for users who prefer using their own infrastructure or are more comfortable with Cassandra. Cassandra is known for its scalability and fault tolerance, making it a suitable choice for long-term data storage.

It's worth noting that Cortex separates index data and chunk data in its storage architecture, allowing users to optimize their infrastructure and costs based on their specific data requirements.

Experimental Features in Cortex

Cortex continuously evolves and introduces experimental features to enhance its capabilities further. Here are a couple of experimental features currently being developed in Cortex:

1. Block-based Ingestion: Cortex is exploring the use of blocks instead of individual chunks for ingestion. This experimental feature aims to optimize storage and reduce disk space usage by efficiently grouping related data samples. While still in the experimental phase, this feature shows promising potential for data compression and storage efficiency.

2. Internal Gossip: Cortex is working to remove the dependency on external systems like Consul or etcd by implementing internal gossip. This experimental feature aims to streamline the deployment and configuration process by reducing the number of external systems required for Cortex to work effectively.

These experimental features highlight Cortex's commitment to innovation and continuous improvement. Users can try out these features and contribute feedback to help Shape the future development of Cortex.

Comparing Cortex with Thanos

When considering a metrics storage solution, one alternative to Cortex that often comes up is Thanos. Thanos is another popular open-source project that focuses on scalable, highly available, and long-term storage for Prometheus data. While Cortex and Thanos share similar goals, there are differences in their architectures and approaches. Let's briefly compare the two:

1. Architecture: Cortex follows a centralized architecture, where metrics data is aggregated and stored in a global store. Thanos, on the other HAND, adopts a decentralized architecture, where data is stored on leaf nodes and relies on object storage for durability.

2. Resource Requirements: Thanos requires less infrastructure resources compared to Cortex because it doesn't need to store index data and relies on object storage for durability. Cortex, with its indexing capabilities, provides more flexibility and performance optimization options but requires additional resources.

3. Operational Complexity: Cortex's centralized architecture simplifies operations and management since all data is stored in one place. Thanos, with its decentralized approach, requires additional configuration and monitoring for the object storage components.

4. Data Retention: Both Cortex and Thanos support long-term data retention. Cortex provides various storage options for users to choose from, while Thanos primarily relies on object storage.

Ultimately, the choice between Cortex and Thanos depends on your specific requirements, infrastructure, and preferences. Evaluating the features, architecture, and trade-offs of each solution will help you make an informed decision.

Choosing Between Cortex and Thanos

If you're trying to decide between Cortex and Thanos for your metrics storage and analysis needs, here are a few factors to consider:

1. Data Aggregation: If you need a centralized system to aggregate data from multiple isolated clusters, Cortex's centralized architecture makes it a natural choice. Cortex allows advanced querying and analysis across multiple clusters, providing a unified view of your metrics data.

2. Scalability Requirements: Both Cortex and Thanos offer scalability, but Cortex's centralized store may be better suited for environments with complex data aggregation needs. Thanos, with its decentralized architecture, is ideal for environments that prioritize scalability over centralized analytics capabilities.

3. Infrastructure Preference: Cortex's support for multiple storage options allows you to choose the infrastructure that aligns with your preferences and existing environment. Whether you prefer cloud-native options like AWS and Google Cloud or prefer running your own infrastructure with Cassandra, Cortex can accommodate your needs.

4. Operational Complexity: Cortex's centralized architecture simplifies operations, as you only need to manage a single system. Thanos, with its decentralized approach and reliance on object storage, introduces additional operational considerations.

By considering these factors and evaluating your specific requirements, you can make an informed decision on whether Cortex or Thanos is the right solution for your metrics storage and analysis needs.

Migrating from Prometheus to Cortex

If you're currently using Prometheus and considering migrating to Cortex, you might be Wondering about the migration process and the potential impact on your data. Currently, there is no easy way to migrate data directly from Prometheus to Cortex. However, there are a few possible approaches you can consider:

1. Take TSDB Snapshot: Prometheus provides an option to take snapshots of its TSDB data. You can take a snapshot and upload it to Cortex, effectively migrating your historical data. This approach requires some manual steps and careful coordination between Prometheus and Cortex instances.

2. Remote Write: Another option is to leverage Cortex's remote write protocol to gradually send data from Prometheus to Cortex. This approach allows you to transition your data from one system to another without significant downtime or data loss. However, it requires configuration and careful planning to ensure a smooth transition.

It's essential to thoroughly test any migration process in a non-production environment before attempting it in a live environment. This will help identify potential challenges and ensure a successful migration without data loss.

Selective Retention in Cortex

Cortex's default retention policy applies equally to all metrics data. However, if you need selective retention for specific types of metrics, Cortex does not provide an out-of-the-box solution for this. Selective retention would require custom configuration or scripting to Apply different retention policies based on specific metric labels or other criteria.

Alternatively, you can consider leveraging external solutions or cloud-based storage options, like AWS S3 lifecycle policies or Google Cloud Storage lifecycle rules, to implement selective retention based on different criteria. These solutions allow you to define rules to automatically transition data to lower-cost or colder storage tiers based on your defined policies.

While Cortex itself doesn't offer native support for selective retention, combining it with external tools or storage options can help you achieve more granular retention policies for your metrics data.

Conclusion

In this guide, we've explored the features, use cases, and architecture of Cortex, a powerful metrics storage and analysis system. Cortex's scalability, long-term storage capabilities, and multi-tenancy support make it a versatile solution for organizations looking to manage and analyze metrics data at scale. Whether you're aggregating data from multiple clusters, providing Prometheus as a service, or conducting custom metrics analysis, Cortex can meet your needs.

As Cortex continues to evolve and introduce experimental features, its potential for innovation and performance improvements grows. By comparing Cortex with other solutions like Thanos and considering your specific requirements, you can make an informed decision on which metrics storage and analysis system is the best fit for your organization.

Are you ready to unlock the full potential of your metrics data? Explore Cortex and experience a new level of scalability, performance, and analytics for your infrastructure monitoring and observability needs.

Frequently Asked Questions (FAQ)

Q: Can Cortex be used without multi-tenancy features?
A: Yes, Cortex can be used without multi-tenancy features. While multi-tenancy is a powerful capability of Cortex, it is not mandatory. You can configure Cortex to work with a single tenant, if desired.

Q: How many companies contribute to the development of Cortex?
A: Multiple companies contribute to the development and maintenance of Cortex. The Core Cortex development team includes individuals from several organizations, including WeWork, Splunk, DigitalOcean, Microsoft, and more.

Q: Is there a formal process for promoting experimental features to stable features in Cortex?
A: Currently, there is no formalized process for promoting experimental features to stable features in Cortex. The decision to promote an experimental feature to stable relies on factors like adoption, stability, and contribution from the user community.

Q: Can I use Cortex and Thanos together?
A: Yes, Cortex and Thanos can be used together, as they serve different purposes. Cortex focuses on scalable metrics storage and analysis, while Thanos provides highly available and long-term storage for Prometheus data. You can leverage both solutions to meet your specific needs and architecture requirements.

Q: Which solution is better for metrics retention: Cortex or Thanos?
A: Both Cortex and Thanos offer long-term metrics retention capabilities. The choice depends on your specific requirements and infrastructure. Cortex provides options for centralized storage and advanced querying, while Thanos focuses on distributed storage and scalability. Evaluating your needs will help you determine which solution is a better fit for your metrics retention requirements.